OHI Science | Citation policy
[REFERENCE RMD FILE: https://cdn.rawgit.com/OHI-Science/ohiprep/master/globalprep/np/v2016/WGI_dataprep.html]
This script downloads WGI data and prepares it for a pressures (1 - WGI) and resilience data layer.
None
Reference: http://info.worldbank.org/governance/wgi/index.aspx#home
Downloaded: Sep 6 2017
Description:
The Worldwide Governance Indicators (WGI) project reports aggregate and individual governance indicators for 215 economies over the period 1996–2015, for six dimensions of governance:
Time range: 1996-2015
library(ohicore) # devtools::install_github('ohi-science/ohicore@dev')
library(tools)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(tidyr)
library(WDI) # install.packages('WDI')## Loading required package: RJSONIOlibrary(stringr)
# comment out when knitting:
# setwd('globalprep/prs_res_wgi/v2017')
# check website to see what years are available: http://info.worldbank.org/governance/wgi/index.aspx#home
yr_start = 1996
yr_end = 2015Download each of the 6 WGI indicators:
## access data ----
## get description of variables:
indicators <- data.frame(WDI_data[[1]])
indicators[grep("VA.EST", indicators$indicator), ]## indicator name
## 6623 VA.EST Voice and Accountability: Estimate
## description
## 6623 Voice and Accountability captures perceptions of the extent to which a country's citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media.
## sourceDatabase
## 6623 Worldwide Governance Indicators
## sourceOrganization
## 6623 Worldwide Governance Indicators, The World Bankindicators[grep("PV.EST", indicators$indicator), ]## indicator
## 4569 PV.EST
## name
## 4569 Political Stability and Absence of Violence/Terrorism: Estimate
## description
## 4569 Political Stability and Absence of Violence/Terrorism captures perceptions of the likelihood that the government will be destabilized or overthrown by unconstitutional or violent means, including politically-motivated violence and terrorism.
## sourceDatabase
## 4569 Worldwide Governance Indicators
## sourceOrganization
## 4569 Worldwide Governance Indicators, The World Bankindicators[grep("GE.EST", indicators$indicator), ]## indicator name
## 2515 GE.EST Government Effectiveness: Estimate
## description
## 2515 Government Effectiveness captures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies.
## sourceDatabase
## 2515 Worldwide Governance Indicators
## sourceOrganization
## 2515 Worldwide Governance Indicators, The World Bankindicators[grep("RQ.EST", indicators$indicator), ]## indicator name
## 4625 RQ.EST Regulatory Quality: Estimate
## description
## 4625 Regulatory Quality captures perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development.
## sourceDatabase
## 4625 Worldwide Governance Indicators
## sourceOrganization
## 4625 Worldwide Governance Indicators, The World Bankindicators[grep("RL.EST", indicators$indicator), ]## indicator name
## 4621 RL.EST Rule of Law: Estimate
## description
## 4621 Rule of Law captures perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence.
## sourceDatabase
## 4621 Worldwide Governance Indicators
## sourceOrganization
## 4621 Worldwide Governance Indicators, The World Bankindicators[grep("CC.EST", indicators$indicator), ]## indicator name
## 689 CC.EST Control of Corruption: Estimate
## description
## 689 Control of Corruption captures perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as ""capture"" of the state by elites and private interests. \nEstimate gives the country's score on the aggregate indicator, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5.
## sourceDatabase
## 689 Worldwide Governance Indicators
## sourceOrganization
## 689 Worldwide Governance Indicators, The World Bank# identify the six indicators
# WDIsearch('violence')# general search
key_voice = WDI(
WDIsearch('Voice and Accountability: Estimate', field='name')['indicator'],
country='all',start = yr_start, end=yr_end)
key_polst = WDI(
WDIsearch('Political Stability and Absence of Violence/Terrorism: Estimate', field='name')['indicator'],
country='all',start = yr_start, end=yr_end)
key_gvtef = WDI(
WDIsearch('Government Effectiveness: Estimate', field='name')['indicator'],
country='all',start = yr_start, end=yr_end)
key_regqt = WDI(
WDIsearch('Regulatory Quality: Estimate', field='name')['indicator'],
country='all',start = yr_start, end=yr_end)
key_rolaw = WDI(
WDIsearch('Rule of Law: Estimate', field='name')['indicator'],
country='all',start = yr_start, end=yr_end)
key_corrp = WDI(
WDIsearch('Control of Corruption: Estimate', field='name')['indicator'],
country='all',start = yr_start, end=yr_end)Combine the indicators.
d = key_voice %>%
select(country, year, VA.EST) %>%
left_join(key_polst %>% select(-iso2c), by=(c('country', 'year'))) %>%
left_join(key_gvtef %>% select(-iso2c), by=(c('country', 'year'))) %>%
left_join(key_regqt %>% select(-iso2c), by=(c('country', 'year'))) %>%
left_join(key_rolaw %>% select(-iso2c), by=(c('country', 'year'))) %>%
left_join(key_corrp %>% select(-iso2c), by=(c('country', 'year'))); head(d); summary(d); sapply(d, class) ## country year VA.EST PV.EST GE.EST RQ.EST RL.EST CC.EST
## 1 Anguilla 2015 NA 1.249272 1.007546 0.9129404 -0.03924862 1.218489
## 2 Anguilla 2014 NA 1.164255 0.952229 0.8850665 0.01886257 1.225222
## 3 Anguilla 2013 1.0047673 1.533980 1.539397 1.3272622 1.36789608 1.260798
## 4 Anguilla 2012 1.0256717 1.476920 1.531356 1.3278290 1.36444592 1.268373
## 5 Anguilla 2011 1.0090517 1.550928 1.504689 1.3720528 1.36059737 1.279277
## 6 Anguilla 2010 0.9882845 1.373762 1.518033 1.3916917 1.38212872 1.308072## country year VA.EST PV.EST
## Length:3638 Min. :1996 Min. :-2.31340 Min. :-3.3149
## Class :character 1st Qu.:2003 1st Qu.:-0.82307 1st Qu.:-0.6665
## Mode :character Median :2007 Median : 0.03926 Median : 0.1204
## Mean :2007 Mean : 0.00000 Mean : 0.0000
## 3rd Qu.:2011 3rd Qu.: 0.93212 3rd Qu.: 0.8551
## Max. :2015 Max. : 1.80099 Max. : 1.9431
## NA's :110 NA's :159
## GE.EST RQ.EST RL.EST CC.EST
## Min. :-2.4459 Min. :-2.6450 Min. :-2.6065 Min. :-1.8687
## 1st Qu.:-0.7399 1st Qu.:-0.7071 1st Qu.:-0.7897 1st Qu.:-0.7653
## Median :-0.1527 Median :-0.1165 Median :-0.1294 Median :-0.2381
## Mean : 0.0000 Mean : 0.0000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.7483 3rd Qu.: 0.8022 3rd Qu.: 0.8264 3rd Qu.: 0.7329
## Max. : 2.4370 Max. : 2.2605 Max. : 2.1003 Max. : 2.4700
## NA's :167 NA's :167 NA's :95 NA's :153## country year VA.EST PV.EST GE.EST RQ.EST
## "character" "numeric" "numeric" "numeric" "numeric" "numeric"
## RL.EST CC.EST
## "numeric" "numeric"# archived record of raw data: write.csv(d, file.path('raw', 'worldbank_wgi_from_wdi_api.csv'), row.names=F)The first gapfilling occurs when we use the average of previous years data within each country/indicator. This occurs when a country has data, but not for all years.
Countries without 4 or more indicators are cut (gapfilled later).
d <- read.csv('raw/worldbank_wgi_from_wdi_api.csv')
d <- gather(d, "indicator", "value", VA.EST:CC.EST)
## each country has 17 years of data
d_gap_fill <- d %>%
group_by(country, year) %>%
mutate(NA_count_c_y = sum(is.na(value))) %>% # get count of NA values for the indicators prior to gapfilling, max value is 6 ()
ungroup() %>%
group_by(country, indicator) %>% # this section gap-fills with the mean of values across years within the same region/indicator
mutate(ind_mean_c_i = mean(value, na.rm=TRUE)) %>%
ungroup() %>%
mutate(value = ifelse(is.na(value), ind_mean_c_i, value)) %>%
group_by(country, year) %>%
mutate(NA_count_post_gf1 = sum(is.na(value))) #count NA values for the num of indicators after last gap-fill (some values will be gapfilled by other years of data)
## cut regions with <4 indicators to calculate score:
countries_no_data <- d_gap_fill %>%
filter(NA_count_post_gf1 > 3)
countries_no_data <- unique(countries_no_data$country)
countries_no_data## [1] New Caledonia
## 215 Levels: Afghanistan Albania Algeria American Samoa Andorra ... Zimbabwe# In this case, the countries with minimal data (< 4 indicators ever calculated) are deleted.
# These will be gap-filled later on if they are deleted now.
d_gap_fill <- d_gap_fill %>%
filter(!(country %in% countries_no_data))This involves: * taking the average of the 6 indicators (assuming there are at least 4 of the 6 indicators) * rescaling the data from 0 to 1
d_calcs <- d_gap_fill %>%
group_by(country, year) %>%
summarize(score_wgi_scale = mean(value, na.rm=T),
NA_start = mean(NA_count_c_y),
NA_post_gf_1 = mean(NA_count_post_gf1)) %>%
ungroup()
wgi_range = c(-2.5, 2.5)
d_calcs <- d_calcs %>%
mutate(score = (score_wgi_scale - wgi_range[1]) / (wgi_range[2] - wgi_range[1])) %>%
ungroup(); head(d_calcs); summary(d_calcs)## # A tibble: 6 x 6
## country year score_wgi_scale NA_start NA_post_gf_1 score
## <fctr> <int> <dbl> <dbl> <dbl> <dbl>
## 1 Afghanistan 1996 -2.069751 0 0 0.08604986
## 2 Afghanistan 1998 -2.095261 0 0 0.08094787
## 3 Afghanistan 2000 -2.122886 0 0 0.07542271
## 4 Afghanistan 2002 -1.747802 0 0 0.15043956
## 5 Afghanistan 2003 -1.570560 0 0 0.18588806
## 6 Afghanistan 2004 -1.510622 0 0 0.19787561## country year score_wgi_scale NA_start
## Afghanistan : 17 Min. :1996 Min. :-2.490863 Min. :0.0000
## Albania : 17 1st Qu.:2003 1st Qu.:-0.676018 1st Qu.:0.0000
## Algeria : 17 Median :2007 Median :-0.112269 Median :0.0000
## American Samoa: 17 Mean :2007 Mean : 0.008306 Mean :0.2389
## Andorra : 17 3rd Qu.:2011 3rd Qu.: 0.740349 3rd Qu.:0.0000
## Angola : 17 Max. :2015 Max. : 1.985402 Max. :6.0000
## (Other) :3536
## NA_post_gf_1 score
## Min. :0.00000 Min. :0.001827
## 1st Qu.:0.00000 1st Qu.:0.364797
## Median :0.00000 Median :0.477546
## Mean :0.02804 Mean :0.501661
## 3rd Qu.:0.00000 3rd Qu.:0.648070
## Max. :3.00000 Max. :0.897080
## # document gapfilling
d_calcs <- d_calcs %>%
mutate(gap_fill = NA_start - NA_post_gf_1, # if there are values in NA_post_gf_1, it means these weren't gapfilled
gap_fill = ifelse(is.na(score), 0, gap_fill)) %>%
select(-NA_start, -NA_post_gf_1)
d_calcs[d_calcs$gap_fill>0, ] ## # A tibble: 192 x 5
## country year score_wgi_scale score gap_fill
## <fctr> <int> <dbl> <dbl> <dbl>
## 1 American Samoa 1996 0.7170524 0.6434105 6
## 2 American Samoa 1998 0.7170524 0.6434105 6
## 3 American Samoa 2000 0.7170524 0.6434105 6
## 4 American Samoa 2002 0.7170524 0.6434105 6
## 5 American Samoa 2003 0.7170524 0.6434105 6
## 6 American Samoa 2014 0.8731227 0.6746245 1
## 7 American Samoa 2015 0.8914434 0.6782887 1
## 8 Anguilla 1996 1.2247660 0.7449532 6
## 9 Anguilla 1998 1.2247660 0.7449532 6
## 10 Anguilla 2000 1.2247660 0.7449532 6
## # ... with 182 more rowsd_calcs[d_calcs$country == "New Caledonia", ] # no data, was deleted earlier## # A tibble: 0 x 5
## # ... with 5 variables: country <fctr>, year <int>, score_wgi_scale <dbl>,
## # score <dbl>, gap_fill <dbl>d_calcs[d_calcs$country == "Niue", ] # should have gap-fill values between 0-6## # A tibble: 17 x 5
## country year score_wgi_scale score gap_fill
## <fctr> <int> <dbl> <dbl> <dbl>
## 1 Niue 1996 -0.3520579 0.4295884 6
## 2 Niue 1998 -0.3520579 0.4295884 6
## 3 Niue 2000 -0.3520579 0.4295884 6
## 4 Niue 2002 -0.3520579 0.4295884 6
## 5 Niue 2003 -0.3520579 0.4295884 6
## 6 Niue 2004 -0.3520579 0.4295884 6
## 7 Niue 2005 -0.3520579 0.4295884 6
## 8 Niue 2006 -0.3520579 0.4295884 6
## 9 Niue 2007 -0.3520579 0.4295884 6
## 10 Niue 2008 -0.3520579 0.4295884 6
## 11 Niue 2009 -0.3812502 0.4237500 0
## 12 Niue 2010 -0.3347712 0.4330458 0
## 13 Niue 2011 -0.3390155 0.4321969 1
## 14 Niue 2012 -0.3533298 0.4293340 1
## 15 Niue 2013 -0.3519228 0.4296154 1
## 16 Niue 2014 -0.3520579 0.4295884 6
## 17 Niue 2015 -0.3520579 0.4295884 6## save intermediate file of wgi scores pre-gapfilling (for OHI+ use)
write.csv(d_calcs %>%
select(country, year, score_wgi_scale, score_ohi_scale = score),
file.path('intermediate/wgi_combined_scores_by_country.csv'),
row.names = FALSE)## We report these regions at a greater spatial resolution:
## Aruba is part of the Netherlands Antilles, but it is reported separately
country_split_1 <- data.frame(country = "Netherlands Antilles", region = c('Bonaire', 'Curacao', 'Saba', 'Sint Maarten', 'Sint Eustatius'))
country_split_2 <- data.frame(country = "Jersey, Channel Islands", region = c('Jersey', 'Guernsey'))
country_split <- rbind(country_split_1, country_split_2)
country_split_data <- country_split %>%
left_join(d_calcs) %>%
select(-country) %>%
rename(country = region)## Joining, by = "country"## Warning: Column `country` joining factors with different levels, coercing
## to character vectord_calcs <- d_calcs %>%
filter(!(country %in% c("Netherlands Antilles", "Jersey, Channel Islands"))) %>%
rbind(country_split_data) %>%
mutate(country = as.character(country))
d_calcs$country[grep("Korea, Dem.", d_calcs$country)] <- "North Korea"
### Function to convert to OHI region ID
d_calcs_rgn <- name_2_rgn(df_in = d_calcs,
fld_name='country',
flds_unique=c('year'))##
## These data were removed for not being of the proper rgn_type (eez,ohi_region) or mismatching region names in the lookup tables:
## tmp_type
## tmp_name disputed landlocked
## Afghanistan 0 17
## Andorra 0 17
## Armenia 0 17
## Austria 0 17
## Belarus 0 17
## Bhutan 0 17
## Bolivia 0 17
## Botswana 0 17
## Burkina Faso 0 17
## Burundi 0 17
## Central African Republic 0 17
## Chad 0 17
## Czech Republic 0 17
## Ethiopia 0 17
## Hungary 0 17
## Kazakhstan 0 17
## Kosovo 0 17
## Kyrgyz Republic 0 17
## Lao PDR 0 17
## Lesotho 0 17
## Liechtenstein 0 17
## Luxembourg 0 17
## Macedonia, FYR 0 17
## Malawi 0 17
## Mali 0 17
## Moldova 0 17
## Mongolia 0 17
## Nepal 0 17
## Niger 0 17
## Paraguay 0 17
## Rwanda 0 17
## San Marino 0 17
## Serbia 0 17
## Slovak Republic 0 17
## South Sudan 0 17
## Swaziland 0 17
## Switzerland 0 17
## Tajikistan 0 17
## Turkmenistan 0 17
## Uganda 0 17
## Uzbekistan 0 17
## West Bank and Gaza 17 0
## Zambia 0 17
## Zimbabwe 0 17##
## DUPLICATES found. Consider using collapse2rgn to collapse duplicates (function in progress).## # A tibble: 5 x 1
## country
## <chr>
## 1 China
## 2 Hong Kong SAR, China
## 3 Macao SAR, China
## 4 Puerto Rico
## 5 Virgin Islands (U.S.)### Combine the duplicate regions (we report these at lower resolution)
### In this case, we take the weighted average
population_weights <- data.frame(country = c("Virgin Islands (U.S.)", "Puerto Rico",
"China", "Hong Kong SAR, China", "Macao SAR, China"),
population = c(106405, 3725789,
1339724852, 7071576, 636200))
d_calcs_rgn <- d_calcs_rgn %>%
left_join(population_weights, by="country") %>%
mutate(population = ifelse(is.na(population), 1, population)) %>%
group_by(rgn_id, year) %>%
summarize(score = weighted.mean(score, population),
gapfill_within_rgn = weighted.mean(gap_fill, population)) %>%
ungroup() %>%
filter(rgn_id <= 250)## Warning: Column `country` joining character vector and factor, coercing
## into character vectorsummary(d_calcs_rgn)## rgn_id year score gapfill_within_rgn
## Min. : 6.0 Min. :1996 Min. :0.001827 Min. :0.0000
## 1st Qu.: 65.0 1st Qu.:2003 1st Qu.:0.389852 1st Qu.:0.0000
## Median :126.0 Median :2007 Median :0.513044 Median :0.0000
## Mean :124.8 Mean :2007 Mean :0.520883 Mean :0.2982
## 3rd Qu.:186.0 3rd Qu.:2011 3rd Qu.:0.663986 3rd Qu.:0.0000
## Max. :250.0 Max. :2015 Max. :0.897080 Max. :6.0000This gapfilling occurs when we use assign a territorial region the value of their parent country.
## data that describes territories of countries
territory = rgn_master %>%
select(rgn_id = rgn_id_2013,
sov_id) %>%
group_by(rgn_id) %>% # remove duplicated countries from this rgn_id list
summarize(sov_id = mean(sov_id, na.rm=T)) %>% # duplicates always have the same sov_id (r2 value)
filter(rgn_id <= 250, rgn_id != 213)
## expand to include all years of data
territory <- data.frame(year=yr_start:yr_end) %>%
merge(territory, by=NULL)
# assign territories the values of their country
d_sovs = d_calcs_rgn %>%
full_join(territory, by = c('rgn_id', 'year')) %>%
group_by(sov_id, year) %>%
mutate(score_gf_territory = mean(score, na.rm=TRUE),
gapfill_within_rgn = mean(gapfill_within_rgn, na.rm=TRUE))%>%
filter(!is.na(gapfill_within_rgn)) %>%
ungroup()
head(d_sovs)## # A tibble: 6 x 6
## rgn_id year score gapfill_within_rgn sov_id score_gf_territory
## <int> <int> <dbl> <dbl> <dbl> <dbl>
## 1 6 1996 0.5140133 3 6 0.5140133
## 2 6 1998 0.5039277 0 6 0.5039277
## 3 6 2000 0.5131196 0 6 0.5131196
## 4 6 2002 0.4764124 0 6 0.4764124
## 5 6 2003 0.4521804 0 6 0.4521804
## 6 6 2004 0.4817330 0 6 0.4817330summary(d_sovs)## rgn_id year score gapfill_within_rgn
## Min. : 1.00 Min. :1996 Min. :0.0018 Min. :0.0000
## 1st Qu.: 58.75 1st Qu.:2003 1st Qu.:0.3899 1st Qu.:0.0000
## Median :116.50 Median :2007 Median :0.5130 Median :0.0000
## Mean :117.64 Mean :2007 Mean :0.5209 Mean :0.4015
## 3rd Qu.:176.25 3rd Qu.:2011 3rd Qu.:0.6640 3rd Qu.:0.0000
## Max. :250.00 Max. :2015 Max. :0.8971 Max. :6.0000
## NA's :833
## sov_id score_gf_territory
## Min. : 6.00 Min. :0.001827
## 1st Qu.: 73.75 1st Qu.:0.423768
## Median :163.00 Median :0.580945
## Mean :133.72 Mean :0.560252
## 3rd Qu.:180.00 3rd Qu.:0.695679
## Max. :247.00 Max. :0.897080
## # format these data
d_gf2 <- d_sovs %>%
mutate(gapfill_territory = ifelse(is.na(score) & !is.na(score_gf_territory), "territory", "NA")) %>%
mutate(score = ifelse(is.na(score), score_gf_territory, score)) %>%
select(rgn_id, year, score, gapfill_within_rgn, gapfill_territory)
# make sure that all regions have a score
regions <- rgn_master %>%
filter(rgn_typ == "eez") %>%
filter(rgn_id_2013 <= 250) %>%
filter(rgn_id_2013 != 213) %>%
select(rgn_id = rgn_id_2013) %>%
unique() %>%
arrange(rgn_id)
d_gf2 <- regions %>%
left_join(d_gf2)## Joining, by = "rgn_id"## check for NA values within "score" variable
## if so, need to gapfill using UN geopolitical regions
summary(d_gf2)## rgn_id year score gapfill_within_rgn
## Min. : 1.00 Min. :1996 Min. :0.001827 Min. :0.0000
## 1st Qu.: 58.75 1st Qu.:2003 1st Qu.:0.423459 1st Qu.:0.0000
## Median :116.50 Median :2007 Median :0.578439 Median :0.0000
## Mean :117.64 Mean :2007 Mean :0.560252 Mean :0.4015
## 3rd Qu.:176.25 3rd Qu.:2011 3rd Qu.:0.694920 3rd Qu.:0.0000
## Max. :250.00 Max. :2015 Max. :0.897080 Max. :6.0000
## gapfill_territory
## Length:3740
## Class :character
## Mode :character
##
##
## These regions will receive an NA for their score (when established population is < 100 people). We decided it would be better to give uninhabited regions the scores of their administrative countries.
# uninhab <- read.csv('../../../src/LookupTables/rgn_uninhabited_islands.csv') %>%
# filter(is.na(est_population) | est_population < 100)
#
# d_gf2 <- d_gf2 %>%
# mutate(score = ifelse(rgn_id %in% uninhab$rgn_id, NA, score))Comparing this year’s values against last year’s. These should be the same unless there have been updates to WGI source data or a change to methods. For this year, there was a small change that effected a few territorial regions. In the past, we used the sovereign country value, but in the case, we averaged the sovereign country and the available territorial values. For example,
Also look at top/bottom 10 regions to make sure these seem reasonable.
new2014 <- d_gf2 %>%
filter(year==2014) %>%
select(rgn_id, score)
old2014 <- read.csv('../v2016/output/wgi_res_2016.csv') %>%
select(rgn_id, old_score=resilience_score) %>%
full_join(new2014)## Joining, by = "rgn_id"## should be a 1:1 relationship
plot(old_score ~ score, data=old2014)
abline(0,1, col="red")
## Top/Bottom 10 scorers:
# get region names
regions <- rgn_master %>%
filter(rgn_typ == "eez") %>%
filter(rgn_id_2013 <= 250) %>%
filter(rgn_id_2013 != 213) %>%
select(rgn_id = rgn_id_2013, rgn_name=rgn_nam_2013) %>%
unique() %>%
arrange(rgn_id)
tmp <- d_gf2 %>%
filter(year==2015) %>%
arrange(score) %>%
select(rgn_id, score) %>%
left_join(regions)## Joining, by = "rgn_id"tmp[1:10, ]## rgn_id score rgn_name
## 1 44 0.07284121 Somalia
## 2 77 0.13275811 Syria
## 3 67 0.13726052 Libya
## 4 21 0.16035603 North Korea
## 5 49 0.17821968 Sudan
## 6 47 0.18152779 Yemen
## 7 45 0.18735098 Eritrea
## 8 199 0.19150840 Democratic Republic of the Congo
## 9 192 0.20620900 Iraq
## 10 139 0.21579751 Venezuelatmp[211:220, ]## rgn_id score rgn_name
## 211 208 0.8207867 Singapore
## 212 218 0.8282229 Canada
## 213 177 0.8312352 Netherlands
## 214 175 0.8438872 Denmark
## 215 222 0.8490212 Sweden
## 216 174 0.8535604 Finland
## 217 105 0.8543166 Bouvet Island
## 218 144 0.8543166 Jan Mayen
## 219 223 0.8543166 Norway
## 220 162 0.8738509 New Zealandhist(tmp$score)
Gapfilling and score data are saved for each scenario year.
tmp_data_res <- d_gf2 %>%
select(rgn_id, year, resilience_score = score)
write.csv(tmp_data_res, "output/wgi_res.csv", row.names=FALSE)
tmp_data_prs <- d_gf2 %>%
mutate(score = 1 - score) %>%
select(rgn_id, year, pressure_score = score)
write.csv(tmp_data_prs, "output/wgi_prs.csv", row.names=FALSE)
tmp_gf <- d_gf2 %>%
select(rgn_id, year, gapfill_within_rgn, gapfill_territory)
write.csv(tmp_gf, "output/wgi_gf.csv", row.names=FALSE)